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Automatic Classification of Label‐Free Cells from Small Cell Lung Cancer and Poorly Differentiated Lung Adenocarcinoma with 2D Light Scattering Static Cytometry and Machine Learning
Author(s) -
Wei Haifeng,
Xie Linyan,
Liu Qiao,
Shao Changshun,
Wang Ximing,
Su Xuantao
Publication year - 2019
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.23671
Subject(s) - adenocarcinoma , cytometry , flow cytometry , lung , lung cancer , cell , medicine , pathology , cancer research , computer science , cancer , biology , immunology , biochemistry
Small cell lung cancer (SCLC) needs to be classified from poorly differentiated lung adenocarcinoma (PDLAC) for appropriate treatment of lung cancer patients. Currently, the classification is achieved by experienced clinicians, radiologists and pathologists based on subjective and qualitative analysis of imaging, cytological and immunohistochemical (IHC) features. Label‐free classification of lung cancer cell lines is developed here by using two‐dimensional (2D) light scattering static cytometric technique. Measurements of scattered light at forward scattering (FSC) and side scattering (SSC) by using conventional cytometry show that SCLC cells are overlapped with PDLAC cells. However, our 2D light scattering static cytometer reveals remarkable differences between the 2D light scattering patterns of SCLC cell lines (H209 and H69) and PDLAC cell line (SK‐LU‐1). By adopting support vector machine (SVM) classifier with leave‐one‐out cross‐validation (LOO‐CV), SCLC and PDLAC cells are automatically classified with an accuracy of 99.87%. Our label‐free 2D light scattering static cytometer may serve as a new, accurate, and easy‐to‐use method for the automatic classification of SCLC and PDLAC cells. © 2018 International Society for Advancement of Cytometry